KAM: an economic mechanism against white-collar AI displacement

Most AI x-risk framing centres on loss of control over powerful systems. We think there's a slower risk running underneath that: the wholesale absorption of professional expertise into AI systems with no mechanism to attribute or compensate the humans who generated it. Rapid, uncompensated displacement of white-collar work will cause unemployment. It erodes the tax base and the professional class that functioning institutions depend on for stability, at precisely the moment those institutions need to be capable of governing transformative AI well. A destabilised society with increasing wealth inequality governs AI badly.

We are working to build answer at organisational scale. The Knowledge Attribution Model tracks what each person contributes to a growing AI capability, weighted by impact, and gives them ongoing attribution for what the model generates from that contribution afterward. It rewards novel research and depth of expertise rather than volume of knowledge handed over: generating new understanding earns more than surrendering what's already known. Where the default path is to extract expertise into a model and discard the person, KAM keeps a durable, ongoing economic link between the human source of knowledge and the value it keeps generating.